Pre-Conference Tutorials and Conference Sessions -
June 11, 2018
| Monday June 11 7:30–8:30 |
Registration and Continental Breakfast | ||||||||||||||
| 8:30 - 11:45 MORNING TUTORIALS | |||||||||||||||
Monday
|
T1: Getting Started with Data Governance and Data Stewardship Malcolm Chisholm, Chief Innovation Officer, First San Francisco Partners Many enterprises now realize that data must be managed well, and from this it is a short step to acknowledging the need for Data Governance. But there is a wide gulf between this acknowledgement and having a minimally viable Data Governance program – meaning one that is self-sustaining. In this tutorial we describe the steps needed to begin a Data Governance program, with particular emphasis on establishing Data Stewardship throughout the enterprise. The organizational units needed for Data Governance and their interrelationships are examined. The creation of a Data Stewardship network across the enterprise, and the various roles of data stewards are discussed. How to interact with executive management, particularly in terms of upward reporting, is also covered. The need for a roadmap that adequately meets the needs of the Data Governance program for the first 2 years is described. Attendees will learn:
Level of Audience |
||||||||||||||
Speaker:
|
|||||||||||||||
 |
|||||||||||||||
Monday
|
T2: Data Governance Value and Sustainability - 4 Case Studies to Learn From John Ladley, Chief Delivery Officer, First San Francisco Partners How can some organizations make progress investing in data governance programs, while others try again and again and still fail? There are always the obvious success factors of sponsorship, engagement, operational structure etc. -- but what if all the essential pieces are in place and your data governance program still isn’t meeting its objective? What subtle, yet critical factors could you be overlooking? In this new tutorial, John will present four case studies of companies that have had varying degrees of success with data governance initiatives. Attendees will leave the session understanding the obvious, and more importantly, not so obvious elements that made the difference between success and failure including:
The case studies will span multiple industries while exploring consistent themes across the process, people, technology and data aspects of each organization to provide tips and methods that might make the difference for your efforts. Level of Audience |
||||||||||||||
Speaker:
|
|||||||||||||||
|
|||||||||||||||
Monday
|
T3: How
to Save a Failing Data Governance Program Robert S. Seiner, President/Publisher, KIK Consulting/TDAN.com Data Governance programs tend to take a long time to get started, to gain momentum and to demonstrate measurable value to the organization. Over this period, programs often fail to sustain the level of enthusiasm that existed when the program was just starting. There are many reasons for this. Management interest wanes, new projects grab people’s interests and work group meetings cease to be efficient and effective. In this tutorial, Bob Seiner will identify and address the reasons why Data Governance programs fall apart or fail to be sustainable over lengthy periods. The initial approach to developing the program has something to do with it but lack of focus on essential program components is the true villain. Bob will share techniques that he has developed to assure long term program health. In this tutorial, Bob will share:
Level of Audience |
||||||||||||||
Speaker:
|
|||||||||||||||
|
|||||||||||||||
Monday
|
T4: Designing
Data Governance & Metadata into Your Data Strategy Donna Burbank, Managing Director, Global Data Strategy, Ltd In today’s data-driven enterprise, building data governance and metadata management into your data strategy can seem more complex than ever. Not only is innovation in technology occurring a more rapid pace than ever before creating more diverse metadata, but as more business stakeholders become involved with data-centric initiatives, “people-centric” initiatives such as data governance increase in importance as well. This workshop demystifies data governance and metadata and provides practical steps in creating a robust data strategy that encompasses people, process, and technology to provide concrete and demonstrable business value. Topics include:
Level of Audience |
||||||||||||||
Speaker:
|
|||||||||||||||
|
|||||||||||||||
Monday
|
T5: A
Roadmap for Building a Successful Data Quality Program: How to Get Started, and
How to Assess and Improve What You Have John Talburt, Professor. University of Arkansas at Little Rock Every organization will say they want high-quality data, but there is still confusion about some of the most fundamental questions. People often ask "What is data quality?" "How is it measured?" "How do I show the value of data quality to management?" "How do I build an effective data quality program? The tutorial is designed to answer these and other related questions and give participants actionable steps to implement a successful data quality management program. Accurate business reporting and data analytics can only be achieved using high-quality data. Yet many organizations either do not have a data quality program, or they just focus on standardizing source data. Having a complete, ongoing program to measure, monitor, and improve the quality of data is a competitive advantage for an organization in today’s data driven economy. This tutorial is primarily for participants starting a comprehensive data quality program or wanting to assess and improve the capabilities of an existing data quality program. Hands on exercises are included. Participants will learn:
Level of Audience |
||||||||||||||
Speaker:
|
|||||||||||||||
|
|||||||||||||||
Monday
|
T6: An
In-depth Review of Data Governance and Big Data Tools Sunil Soares, Founder & Managing Partner, Information Asset This tutorial will cover data governance tools for the following requirements:
Level of Audience |
||||||||||||||
Speaker:
|
|||||||||||||||
| 12:00
- 12:30 SPONSORED SESSIONS - DATA GOVERNANCE AND DATA QUALITY SOLUTIONS |
|||||||||||||||
Monday
|
SAP EIM Solutions Help Deliver Information Excellence Sue Waite, Senior Director, SAP Center of Excellence Mustafa Dadawalla, CSCP, Manager, Global Data Quality, Corning Optical Communications To enable tomorrow’s business successes, the need for “information excellence” is not only desired, it is a fundamental requirement. But what is information excellence? Simply stated, it is the ability to ensure that enterprise data is trusted, complete, and relevant for analytical and operational use cases. Join this session to explore real-world customer examples that illustrate how SAP has helped clients create trust in their data, find and utilize relevant data to deliver enhanced business insights, and govern it within their applications and big data systems to ultimately drive transformational business outcomes. Level of audience: |
||||||||||||||
Speakers:
|
|||||||||||||||
|
|||||||||||||||
Monday
|
Modern Asset Catalogs - The Stepping Stone to AI Bill Lobig, VP, IBM Watson Data & AI Smarter businesses apply AI to learn and continuously evolve the way they work and fend off disruptors. To extract full value from AI, companies need a data strategy that gives them access to all their data – no matter where it lives – in a fully managed, cloud-native environment that seamlessly scales, provides self service and applies modern data governance and cataloging to empower data scientists turbo charge their journey to AI. Learn how IBM Watson Studio and IBM Watson Knowledge Catalog provide all the tools companies need to embed AI, machine learning and deep learning in their business, while extending existing investments and enabling professionals to gain the most from their data to drive smarter business. Level of audience: |
||||||||||||||
Speaker:
|
|||||||||||||||
|
|||||||||||||||
Monday
|
Data Governance is Dead Stan Christiaens, Co-founder and CTO, Collibra Data governance is the secret to delivering the trust and compliance every data citizen needs to help drive their business forward. But for too many organizations, data governance still means control. And lots of it. Isn't it time to think differently about data governance? During this session, Collibra will share why industry leaders are changing their approach to data governance. They'll also highlight the three governance capabilities every organization needs to empower all data citizens to find, understand, and trust their data. Level of audience: |
||||||||||||||
Speaker:
|
|||||||||||||||
| 1:30 - 4:45 AFTERNOON TUTORIALS | |||||||||||||||
Monday
|
T7: Keeping
the Momentum Going in your Data Quality Program Peter Aiken, Founder and President, Data Blueprint Often a burning bush issue will initiate an organizational data quality effort. While helpful with the initial motivation, most organizations will require additional work to create a suitable foundation upon which to base an advantageous data quality engineering program. That is, in order for data quality efforts to truly succeed and deliver promised returns, it is crucial that organizations:
This tutorial will lead you through the various steps along the way with numerous examples and immediate takeaways Level of Audience |
||||||||||||||
Speaker:
|
|||||||||||||||
|
|||||||||||||||
Monday
|
T8: Driving
Data Governance from a Business Viewpoint: Best Practices, Direction and Advice John Ladley, Chief Delivery Officer, First San Francisco Partners NEW CONTENT FOR 2018 If you believe information is truly an asset, then engaging the
entire business is mandatory. If managing information assets is a business
issue, then data governance is a business program. Because so many data
governance programs are being initiated by business demand, many participants
and stakeholders find themselves in new territory.
Level of Audience |
||||||||||||||
Speaker:
|
|||||||||||||||
|
|||||||||||||||
Monday
|
T9: Governing the Business Vocabulary,
Cataloguing and Mapping of the Business Terms and Critical Data Elements (CDE) Lowell Fryman, Services Capability Principal, Collibra Our business glossary is the cornerstone of a great data governance program. That is quite easy to say, but the creation, management, and adoption is quite challenging. Resource, process, technology and metadata tool management are significant. The data governance program should address the definition of business terms, aligning the terms with the critical data elements associated, and communicating the alignment and the usage of the assets in the glossary to enable consumers across the enterprise. While, many organizations really care about the physical columns used in reporting and analytics, we know that we need to align those CDEs to the Business Terms in order to govern our implementations. A Business Glossary is the tool for capturing assets and exposing authoritative content from our data governance initiatives. The glossary is used to communicate understanding and clarity across the enterprise to connect business management and knowledge workers to business information they can find, understand and trust, helping to eliminate misunderstandings that cause lost time, lost opportunities and lost revenue. This tutorial will be helpful for business users, data management and data governance professionals that have been challenged with any of the following issues:
You will learn:
There are NO product screenshots. Level of Audience |
||||||||||||||
Speaker:
|
|||||||||||||||
|
|||||||||||||||
Monday
|
T10: How an Award Winning Data Governance Program Shows Value with Data Governance
and Data Quality Metrics Michele Koch, Director, Enterprise Data Intelligence, Navient Barbara Deemer, VP Financial Systems and Chief Data Steward, Navient Your data governance journey is like travelling down a windy and bumpy road. Inevitably, there will be times when you will be asked to justify the existence of your program and show the value of your data governance efforts. After all, if you can’t quantify it, why are you doing it? This tutorial will provide a detailed, step-by-step account of Navient’s award-winning approach to developing metrics. Topics that will be covered include:
Level of Audience |
||||||||||||||
Speakers:
|
|||||||||||||||
|
|||||||||||||||
Monday
|
T11: Today’s Data Lakes: Data Governance at Ocean-Scale Anthony Algmin, Independent Consultant This tutorial discusses the evolution of data lakes, what they have historically done well, and where they have struggled to deliver. We look at the underlying technologies that have powered data lakes, and some of the more recent technology developments that are helping data lakes become a more complete solution. Then we explore how data governance plays a more important role in data lakes than people believed in the early days of a few years ago. We will outline the key tenets to building effective data lakes, and explain the problems that happen when data governance fails to keep pace with data growth. Key takeaways include:
Level of Audience |
||||||||||||||
Speaker:
|
|||||||||||||||
|
|||||||||||||||
Monday
|
T12: Data
Governance Meets Psychology 101 - How to Influence Individuals Within
Organizations Mike McMorrow, Principal, MMM Data Perspectives Ltd The single biggest challenge to effective data governance is to persuade people across the organization to buy into it in a deep, sustainable way. To be successful persuaders, Data governance professionals should learn from psychology-based tricks and techniques of great salespeople and organization behaviorists. As humans we are sub-consciously flawed
and, somewhat alarmingly, consistently easy to psychologically 'play' - we
are 'rationalizing' rather than 'rational' decision makers. Great salespeople are expert at pushing the right buttons to get us, as individuals, to make the decisions that they want us to make, and to genuinely believe that we personally own those decisions. This section will explore:
Influencing individuals within groups: There is extensive evidence of the psychological impact of group membership on individual behaviors and decisioning, again generally at a sub-conscious level. This section will explore:
Beware, YOU WON'T think of yourself (as a rational being) in quite the same way again, but YOU WILL take away real, practical tips on how to build intense stakeholder buy-in to data governance back in your organization. Level of Audience |
||||||||||||||
Speaker:
|
|||||||||||||||
| 5:00
- 5:45 Conference Sessions |
|||||||||||||||
Monday
|
Industry Special Interest Group - HEALTHCARE A Journey of Data Governance in Healthcare Wendy Bezotte, Data Governance Program Manager, University of Michigan Medical School Patrick Day, Process Manager, Data Governance Office, Cleveland Clinic Kyle Kerbawy, Enterprise Data Architect, University of Michigan Health Information Technology & Services This presentation will highlight the data governance journeys of two Healthcare organizations focusing on challenges, successes, tips and best practices translatable to other healthcare provider institutions. Healthcare is a highly complex industry, which has lagged other industries in adopting data and analytics to its improvement. If we accept the assertion that healthcare is a knowledge-delivery industry - that its primary aim is the application of specialized skills, tools, and knowledge - then it is our obligation to exploit the data assets at our institutions to augment and optimize that aim and ensure data is managed accordingly. In order to achieve this, data governance is a critical success factor that is beginning to be understood as a core competency within the industry. We will briefly review the paths towards data governance that our enterprises have undertaken, underscoring the organizational and cultural challenges that the efforts have encountered. The emphasis of this presentation will be to highlight the major success points, tips, and best practices that should translate to data governance efforts at other health care provider institutions. Level of Audience |
||||||||||||||
Speakers:
|
|||||||||||||||
|
|||||||||||||||
Monday
|
Industry Special Interest Group - FINANCIAL How to Introduce Acceptable Data Governance and Data Quality to a Major Bank Vladislav Repta, Head of BI, Ceska sporitelna, a.s Peter Kmet, DQ Manager, Ceska sporitelna, a.s Introducing Data Governance and Quality to the company is HARD. This is authentic presentation used in Ceska Sporitelna that had finally received understanding and acceptance of Data Governance and Quality roles and principles, enriched with lessons learnt from previous fails and overall recommendations from our journey. A few years ago we received clear and strict message from regulators: “You must increase data quality in a significant manner and fast.” So we started with failing in introducing Data Governance and Quality into an environment of silo thinking and major resistance to any change. This is the original presentation where we introduced Data Governance through 3 layers – metadata, data and organizational structure. We introduced 4 main roles: Data Stewards, Data Custodians, Data Owners and Data Quality Analysts. We have also introduced two separate Data Quality processes for Data Quality Fixing and Monitoring as well as custom made DQ solution to support us in our efforts. We are bringing authentic and hands on approach to a topic that is resonating not only in finance sector. We are not hesitant to show not only success but also our mistakes in order to help the others to avoid them. Level of Audience |
||||||||||||||
Speakers:
|
|||||||||||||||
|
|||||||||||||||
Monday
|
Industry Special Interest Group - PUBLIC SECTOR Winning the Battle for Data Governance at the Department of Interior Office of Natural Resources Revenue John Hovanec, Program Manager & Data Governance Officer, Office of Natural Resources Revenue Ryan Jordan, Chief Data Steward, Office of Natural Resources Revenue Leading the Data Revolution - First-hand account of how the Department of Interior Office of Natural Resources Revenue (ONRR) established a data governance program. Like many other organizations, ONRR has difficulty managing data as a strategic asset. A lot of personnel, effort, and money are spent on enhancing and maintaining our systems, but there was no group whose sole focus was on the data. The result was lack of business input, limited data understanding, conflicting actions between business groups, and numerous workarounds. Two years ago project teams kicked off to set forth a new data strategy. The result: establishing data governance led by the business and working with IT. We will discuss many key activities that led to adoption of data governance from the ground-level employees to senior executives.
Level of Audience |
||||||||||||||
Speakers:
|
|||||||||||||||
|
|||||||||||||||
Monday
|
Industry Special Interest Group - ENERGY Considerations in Governing an Internet of Things Data Lake Mariela Botella, Data Governance Advisor, ExxonMobil Sunil Soares, Founder and Managing Partner, Information Asset This session will focus on key considerations in governing the Data Lake including stewardship, data ingestion, metadata management and data quality. Key topics to be covered include the following:
Level of Audience |
||||||||||||||
Speakers:
|
|||||||||||||||
|
|||||||||||||||
Monday
|
Industry Special Interest Group - INSURANCE The Evolution of Amica's Architecture Review Board Donald LeMay, Data Governance Analyst, Amica Mutual Insurance Sheila Embree, Lead Data Governance Analyst, Amica Mutual Insurance One of the challenges of any data governance effort is truly getting in on the ground floor when architecture decisions are being made. It is one thing to be involved in a project once it's been approved. It is yet another to have an influence on a project's impact to the corporate technology platform. At Amica, we had expected to establish an Architecture Review Board to ensure that our technological standards are adhered to with new projects. However, what we found is that we didn't have strong data principles to serve as a decision base. This presentation will share our journey of creating data principles and establishing an Architecture Review Board. Level of Audience |
||||||||||||||
Speakers:
|
|||||||||||||||
|
|||||||||||||||
